text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> names.append("LWristYaw")
times.append([3, 50])
keys.append([-1.53589, 0.139552])
names.append("RAnklePitch")
times.append([3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23.6, 26.2,
28.4, 30.4, 32.4, 34.4, 37, 39.6, 42.2, 44.4, 46.2, 50])
keys.a... | code_fim | hard | {
"lang": "python",
"repo": "OpenRoberta/robertalab-naoprogram",
"path": "/OpenRobertaNAO/src/main/resources/originalHal.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> names.append("LWristYaw")
times.append([1.52, 3.12, 3.8, 4.92])
keys.append([0.386526, 0.386526, 0.386526, 0.386526])
names.append("RElbowRoll")
times.append([0.64, 1.36, 2.96, 3.64, 4.2, 4.76])
keys.append([1.28093, 1.39752, 1.57239, 1.24105, 1.22571, 0.84... | code_fim | hard | {
"lang": "python",
"repo": "OpenRoberta/robertalab-naoprogram",
"path": "/OpenRobertaNAO/src/main/resources/originalHal.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mkenworthy/metis_hci path: /LMS_APP/plot_LMS_APP.py
from hcipy import *
import numpy as np
from matplotlib import pyplot as plt
if __name__ == "__main__":
amp = read_fits('METIS_APP_20jul2018_amp.fits')
phase = read_fits('METIS_APP_20jul2018_phase.fits')
# ... | code_fim | hard | {
"lang": "python",
"repo": "mkenworthy/metis_hci",
"path": "/LMS_APP/plot_LMS_APP.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> aper11 = Field(aper1.ravel(), pupil_grid)
aper22 = Field(aper2.ravel(), pupil_grid)
aper33 = Field(aper3.ravel(), pupil_grid)
# This detector grid is in the units of telescope_focal_length
detector_grid = make_focal_grid(pupil_grid, wavelength=wavelength_0, q=... | code_fim | hard | {
"lang": "python",
"repo": "mkenworthy/metis_hci",
"path": "/LMS_APP/plot_LMS_APP.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # make psf A
wf1 = Wavefront(aper11, wavelength)
wf1_foc = prop.forward(wf1)
wf2 = Wavefront(aper22, wavelength)
wf2_foc = prop.forward(wf2)
wf3 = Wavefront(aper33, wavelength)
wf3_foc = p... | code_fim | hard | {
"lang": "python",
"repo": "mkenworthy/metis_hci",
"path": "/LMS_APP/plot_LMS_APP.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: M3DV/SimTA path: /utils/cuda.py
import os
import pickle
import torch.nn as nn
def put_var_on_gpu(var, devices, requires_grad=False):
if len(devices) == 1:
var = var.cuda(devices[0])
return var
<|fim_suffix|> if len(devices) == 1:
model = model.cuda(devices[0])
... | code_fim | easy | {
"lang": "python",
"repo": "M3DV/SimTA",
"path": "/utils/cuda.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if len(devices) == 1:
model = model.cuda(devices[0])
else:
model = nn.DataParallel(model, device_ids=devices)
return model<|fim_prefix|># repo: M3DV/SimTA path: /utils/cuda.py
import os
import pickle
import torch.nn as nn
def put_var_on_gpu(var, devices, requires_grad=False... | code_fim | medium | {
"lang": "python",
"repo": "M3DV/SimTA",
"path": "/utils/cuda.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: M3DV/SimTA path: /utils/cuda.py
import os
import pickle
import torch.nn as nn
<|fim_suffix|>def put_model_on_gpu(model, devices):
if len(devices) == 1:
model = model.cuda(devices[0])
else:
model = nn.DataParallel(model, device_ids=devices)
return model<|fim_middle|>
... | code_fim | medium | {
"lang": "python",
"repo": "M3DV/SimTA",
"path": "/utils/cuda.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>new_data_list = {}
order = 0
for keyword in _data.keys():
order += 1
new_data_list[keyword] = order
# 写入数据
with open('数据处理\\输入神经元的数据处理\\6_final_keyword_and_order.json', 'a+', encoding='utf-8') as f:
f.write(json.dumps(new_data_list, ensure_ascii=False))<|fim_prefix|># repo: xingyu321/SZU_g... | code_fim | medium | {
"lang": "python",
"repo": "xingyu321/SZU_gwt_predict_network",
"path": "/数据处理/输入神经元的数据处理/6_GenerateKey-OrderTable.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xingyu321/SZU_gwt_predict_network path: /数据处理/输入神经元的数据处理/6_GenerateKey-OrderTable.py
"""
这个脚本的目的是生成最终的数据：关键词与对应的序号，final_keyword_and_order_6.json
"""
import json
# 读取数据
with open('数据处理\\输入神经元的数据处理\\5_final_word_weights.json', 'r', encoding='utf-8') as f:
_data = json.loads(f.read())
<|fim_... | code_fim | medium | {
"lang": "python",
"repo": "xingyu321/SZU_gwt_predict_network",
"path": "/数据处理/输入神经元的数据处理/6_GenerateKey-OrderTable.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># 写入数据
with open('数据处理\\输入神经元的数据处理\\6_final_keyword_and_order.json', 'a+', encoding='utf-8') as f:
f.write(json.dumps(new_data_list, ensure_ascii=False))<|fim_prefix|># repo: xingyu321/SZU_gwt_predict_network path: /数据处理/输入神经元的数据处理/6_GenerateKey-OrderTable.py
"""
这个脚本的目的是生成最终的数据：关键词与对应的序号，final_keywo... | code_fim | medium | {
"lang": "python",
"repo": "xingyu321/SZU_gwt_predict_network",
"path": "/数据处理/输入神经元的数据处理/6_GenerateKey-OrderTable.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> #build and install driver
command = u'cd "%s"; ./install.sh 2mic' % self.TMP_DIR
self.logger.debug('Respeaker driver install command: %s' % command)
console = EndlessConsole(command, self.__process_status_callback, self.__install_terminated_callback)
console.start()... | code_fim | hard | {
"lang": "python",
"repo": "tangb/cleepmod-respeaker2mic",
"path": "/backend/seeed2micaudiodriver.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tangb/cleepmod-respeaker2mic path: /backend/seeed2micaudiodriver.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import time
import logging
import os
from raspiot.utils import InvalidParameter, MissingParameter
from raspiot.libs.commands.alsa import Alsa
from raspiot.libs.commands.lsmod import ... | code_fim | hard | {
"lang": "python",
"repo": "tangb/cleepmod-respeaker2mic",
"path": "/backend/seeed2micaudiodriver.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
callback (function): function called after installation
params (dict): additional parameters
Returns:
bool: True if install succeed
"""
#clone git repo
self._get_repository()
#build and install driver
... | code_fim | hard | {
"lang": "python",
"repo": "tangb/cleepmod-respeaker2mic",
"path": "/backend/seeed2micaudiodriver.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> parseDict['alignsCmb'] = parser.getCmbFromList(printerFeatures.aligns)
parseDict['charStylesCmb'] = parser.getCmbFromList(printerFeatures.charStyles)
parseDict['cutsCmb'] = parser.getCmbFromList(printerFeatures.cuts)
return parseDict<|fim_prefix|># repo: chaosdorf/labello path: /templa... | code_fim | hard | {
"lang": "python",
"repo": "chaosdorf/labello",
"path": "/templates/base.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> parseDict['charStylesCmb'] = parser.getCmbFromList(printerFeatures.charStyles)
parseDict['cutsCmb'] = parser.getCmbFromList(printerFeatures.cuts)
return parseDict<|fim_prefix|># repo: chaosdorf/labello path: /templates/base.py
from libs import parser, printerFeatures
def getParseDict():
... | code_fim | hard | {
"lang": "python",
"repo": "chaosdorf/labello",
"path": "/templates/base.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chaosdorf/labello path: /templates/base.py
from libs import parser, printerFeatures
def getParseDict():
parseDict = {}
parseDict['sizesCmb'] = '<optgroup label="Outline Sizes">'
parseDict['sizesCmb'] += parser.getCmbFromList(printerFeatures.sizesOutline)
parseDict['sizesCmb'] +... | code_fim | hard | {
"lang": "python",
"repo": "chaosdorf/labello",
"path": "/templates/base.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@register.simple_tag(takes_context=True)
def or_meta_image_url(context):
"""If context request is not available use base url"""
try:
request = context['request']
absolute_url = request.build_absolute_uri(OR_META_IMAGE_URL)
except KeyError:
absolute_url = BASE_URL + OR_... | code_fim | hard | {
"lang": "python",
"repo": "Our-Revolution/site",
"path": "/pages/templatetags/pages_tags.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Our-Revolution/site path: /pages/templatetags/pages_tags.py
from django import template
from django.conf import settings
from pages.models import AlertLevels, NotificationBanner, SplashModal
register = template.Library()
BASE_URL = settings.BASE_URL
GOOGLE_MAPS_PUBLIC_KEY = settings.GOOGLE_MAPS... | code_fim | hard | {
"lang": "python",
"repo": "Our-Revolution/site",
"path": "/pages/templatetags/pages_tags.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return BASE_URL
@register.simple_tag
def candidates_url():
return settings.CANDIDATES_URL
@register.simple_tag
def endorsement_process_url():
return settings.ENDORSEMENT_PROCESS_URL
@register.simple_tag
def get_alert_level_class(value):
"""Pass in alert level value and get back appro... | code_fim | hard | {
"lang": "python",
"repo": "Our-Revolution/site",
"path": "/pages/templatetags/pages_tags.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lizzij/LCProj path: /sat/code/util.py
import argparse
import logging
import os
import pdb
import random
from collections import namedtuple, defaultdict
from os.path import join
import numpy as np
import scipy.sparse as sparse
import torch
import yaml
logger = logging.getLogger(__name__)
DataSa... | code_fim | hard | {
"lang": "python",
"repo": "lizzij/LCProj",
"path": "/sat/code/util.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> log_file = join(config['dir'], 'train.log')
logging.basicConfig(
handlers=[logging.FileHandler(log_file, mode='w'), logging.StreamHandler()],
format='%(asctime)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
)
logger.setLevel(getattr(logging, config['log_level'].u... | code_fim | hard | {
"lang": "python",
"repo": "lizzij/LCProj",
"path": "/sat/code/util.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: daman2412/iosfu path: /iosfu/backup.py
from __future__ import with_statement
from os import listdir
from os.path import join as join_paths, basename, isdir, isfile
from plistlib import readPlist
from biplist import readPlist as readBinaryPlist
from .conf import BACKUPS_PATH, BACKUP_DEFAULT_SET... | code_fim | hard | {
"lang": "python",
"repo": "daman2412/iosfu",
"path": "/iosfu/backup.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, path):
self.path = path
self.get_info()
self._data_file = self.get_data_file()
self.init_check()
self.read_data_file()
@property
def name(self):
name = self.data('name') or self.id
return name
def get_data_file(se... | code_fim | hard | {
"lang": "python",
"repo": "daman2412/iosfu",
"path": "/iosfu/backup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def parse_response_content(self, response_content):
response = super(AlipayOpenRequestBatchSendResponse, self).parse_response_content(response_content)
if 'response_body' in response:
self.response_body = response['response_body']<|fim_prefix|># repo: alipay/alipay-sdk-pyth... | code_fim | medium | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenRequestBatchSendResponse.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @response_body.setter
def response_body(self, value):
self._response_body = value
def parse_response_content(self, response_content):
response = super(AlipayOpenRequestBatchSendResponse, self).parse_response_content(response_content)
if 'response_body' in response:
... | code_fim | hard | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenRequestBatchSendResponse.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alipay/alipay-sdk-python-all path: /alipay/aop/api/response/AlipayOpenRequestBatchSendResponse.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class AlipayOpenRequestBatchSendResponse(AlipayResponse):
def __ini... | code_fim | medium | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenRequestBatchSendResponse.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cmattey/leetcode_problems path: /Python/lc_122_best_time_buy_sell_stock_ii.py
# Time: O(n), where n = len(prices)
# Space: O(1)
# April 5th
# Time: O(n)
# Space: O(1)
class Solution:
def maxProfit(self, prices: List[int]) -> int:
nums = prices
profit = 0
start, e... | code_fim | hard | {
"lang": "python",
"repo": "cmattey/leetcode_problems",
"path": "/Python/lc_122_best_time_buy_sell_stock_ii.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if prices[index]>min_price:
while index<len(prices) and prices[index]>prices[index-1]:
index+=1
profit+=(prices[index-1]-min_price)
if index<len(prices):
min_price = prices[index]
index+=1
re... | code_fim | hard | {
"lang": "python",
"repo": "cmattey/leetcode_problems",
"path": "/Python/lc_122_best_time_buy_sell_stock_ii.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self,
ctx: RiotGamesApiContext,
session: Optional[Session] = None
):
self._ctx = ctx
self._session = session or Session()
def _request(
self,
path: str,
platform: Optional[str] = None,
params: ... | code_fim | hard | {
"lang": "python",
"repo": "esbraff/riot_games_api",
"path": "/src/riot_games_api/base.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _request(
self,
path: str,
platform: Optional[str] = None,
params: Optional[Dict[str, Any]] = None
):
try:
headers = {
"X-Riot-Token": self._ctx.token
}
base_url = self._ctx.get_base_url... | code_fim | hard | {
"lang": "python",
"repo": "esbraff/riot_games_api",
"path": "/src/riot_games_api/base.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: esbraff/riot_games_api path: /src/riot_games_api/base.py
from requests import Session, RequestException
from typing import Optional, Any, Dict
from urllib.parse import urljoin
from .exceptions import RiotGamesApiException
from .context import RiotGamesApiContext
<|fim_suffix|> retur... | code_fim | hard | {
"lang": "python",
"repo": "esbraff/riot_games_api",
"path": "/src/riot_games_api/base.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='estudios',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='estudios', to=settings.AUTH_USER_MODEL),
),
]<|fim_prefix|># repo: JVacca12/FIRST path: ... | code_fim | medium | {
"lang": "python",
"repo": "JVacca12/FIRST",
"path": "/estudios/migrations/0003_alter_estudios_user.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('estudios', '0002_alter_estudios_fecha_fin'),
]
operations = [
migrations.AlterField(
model_name='estudios',
name='user',
field=models.ForeignKey(on_delete... | code_fim | medium | {
"lang": "python",
"repo": "JVacca12/FIRST",
"path": "/estudios/migrations/0003_alter_estudios_user.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JVacca12/FIRST path: /estudios/migrations/0003_alter_estudios_user.py
# Generated by Django 3.2.7 on 2021-09-30 04:08
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
<|fim_suffix|>
dependencies = [
migrations.swappable_depen... | code_fim | medium | {
"lang": "python",
"repo": "JVacca12/FIRST",
"path": "/estudios/migrations/0003_alter_estudios_user.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> out_sim_score (boolean): flag to indicate whether similarity score
should be included in the output table (defaults to True). Setting
this flag to True will add a column named '_sim_score' in the
output table. This column will contain the similari... | code_fim | hard | {
"lang": "python",
"repo": "anhaidgroup/py_stringsimjoin",
"path": "/py_stringsimjoin/join/overlap_join.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: anhaidgroup/py_stringsimjoin path: /py_stringsimjoin/join/overlap_join.py
def overlap_join(ltable, rtable,
l_key_attr, r_key_attr,
l_join_attr, r_join_attr,
tokenizer, threshold, comp_op='>=',
allow_missing=False,
... | code_fim | hard | {
"lang": "python",
"repo": "anhaidgroup/py_stringsimjoin",
"path": "/py_stringsimjoin/join/overlap_join.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rsagroup/rsatoolbox path: /src/rsatoolbox/inference/result.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Result object definition
"""
import numpy as np
import scipy.stats
import rsatoolbox.model
from rsatoolbox.io.hdf5 import read_dict_hdf5, write_dict_hdf5
from rsatoolbox.io.pkl import... | code_fim | hard | {
"lang": "python",
"repo": "rsagroup/rsatoolbox",
"path": "/src/rsatoolbox/inference/result.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_ci(self, ci_percent, test_type='t-test'):
""" returns confidence intervals for the evaluations"""
prop_cut = (1 - ci_percent) / 2
if test_type == 'bootstrap':
perf = self.evaluations
while len(perf.shape) > 2:
perf = np.nanmean(pe... | code_fim | hard | {
"lang": "python",
"repo": "rsagroup/rsatoolbox",
"path": "/src/rsatoolbox/inference/result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ returns confidence intervals for the evaluations"""
prop_cut = (1 - ci_percent) / 2
if test_type == 'bootstrap':
perf = self.evaluations
while len(perf.shape) > 2:
perf = np.nanmean(perf, axis=-1)
framed_evals = np.concatenate... | code_fim | hard | {
"lang": "python",
"repo": "rsagroup/rsatoolbox",
"path": "/src/rsatoolbox/inference/result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: satyampandeygit/ds-algo-solutions path: /Algorithms/Implementation/Beautiful Days at the Movies/solution.py
#!/bin/python3
import math
import os
import random
import re
import sys
# Complete the beautifulDays function below.
def beautifulDays(i, j, k):
<|fim_suffix|> ijk = input().split()
... | code_fim | hard | {
"lang": "python",
"repo": "satyampandeygit/ds-algo-solutions",
"path": "/Algorithms/Implementation/Beautiful Days at the Movies/solution.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = beautifulDays(i, j, k)
fptr.write(str(result) + '\n')
fptr.close()<|fim_prefix|># repo: satyampandeygit/ds-algo-solutions path: /Algorithms/Implementation/Beautiful Days at the Movies/solution.py
#!/bin/python3
import math
import os
import random
import re
import sys
# Complete t... | code_fim | hard | {
"lang": "python",
"repo": "satyampandeygit/ds-algo-solutions",
"path": "/Algorithms/Implementation/Beautiful Days at the Movies/solution.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print("Removendo tags XML de %s" % caminho)
text = re.sub('<[^<]+>', "", open(caminho, mode='r', encoding='utf8').read())
with open(caminho, "w", encoding='utf8') as f:
f.write(text)
print("Tags XML removidas de %s" % caminho)
remover_tags_xml(caminho_arquivo)<|fim_prefix|># ... | code_fim | easy | {
"lang": "python",
"repo": "pvcastro/1-billion-word-language-modeling-benchmark",
"path": "/scripts/strip_xml.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pvcastro/1-billion-word-language-modeling-benchmark path: /scripts/strip_xml.py
import re, sys
if len(sys.argv) == 2:
caminho_arquivo = sys.argv[1]
print(sys.argv)
else:
print("Usage: python strip_xml.py caminho_arquivo")
sys.exit()
<|fim_suffix|> print("Removendo tags XML de... | code_fim | easy | {
"lang": "python",
"repo": "pvcastro/1-billion-word-language-modeling-benchmark",
"path": "/scripts/strip_xml.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> args = self.build_args
# check essential arguments
for arg in JE_BUILD_ARGS:
if arg not in args:
return ERR_MISSING_ARGUMENT, f'Missing required argument "{arg}".'
# check "format"
if 'format' not in args or args['format'] is None:
... | code_fim | hard | {
"lang": "python",
"repo": "Teahouse-Studios/memepack-builder",
"path": "/memepack_builder/JEPackBuilder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __get_mod_content(self, *mod_files) -> dict:
mods = {}
for file in mod_files:
if file.endswith(".json"):
mods |= json.load(
open(os.path.join(self.mods_path, file), 'r', encoding='utf8'))
elif file.endswith(".lang"):
... | code_fim | hard | {
"lang": "python",
"repo": "Teahouse-Studios/memepack-builder",
"path": "/memepack_builder/JEPackBuilder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Teahouse-Studios/memepack-builder path: /memepack_builder/JEPackBuilder.py
__all__ = [
'PACK_LEGACY_FORMAT', 'PACK_CURRENT_FORMAT', 'JEPackBuilder'
]
import json
import os
from zipfile import ZipFile, ZIP_DEFLATED
from memepack_builder._internal.pack_builder import PackBuilder, LICENSE_FILE
f... | code_fim | hard | {
"lang": "python",
"repo": "Teahouse-Studios/memepack-builder",
"path": "/memepack_builder/JEPackBuilder.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pilliq/balance path: /balance/repay_book.py
# AMDG
import logging
from balance_book import BalanceBook
from itertools import islice
class RepayBook(object):
"""
RepayBook keeps track of entries that have been and need to be repaid to
other people. It keeps these two types of entrie... | code_fim | hard | {
"lang": "python",
"repo": "pilliq/balance",
"path": "/balance/repay_book.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _split_entries(self):
repay = []
repaid = []
for e in self._raw_entries:
if e.category == 'repay':
repaid.append(e)
else:
repay.append(e)
return BalanceBook(repay), BalanceBook(repaid)
def _parse_eid(sel... | code_fim | hard | {
"lang": "python",
"repo": "pilliq/balance",
"path": "/balance/repay_book.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _positize_repay(self):
"""
Make amounts in self.repay positive
"""
for e in self._repay.entries:
e.amount = abs(e.amount)
@property
def entries(self):
return self._repay.entries + self._repaid.entries
@property
def repay(self):
... | code_fim | hard | {
"lang": "python",
"repo": "pilliq/balance",
"path": "/balance/repay_book.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if os.path.isfile(FLAT_FILE_W):
pkl_file = open(FLAT_FILE_W, 'rb')
wflat = pk.load(pkl_file)
pkl_file.close()
else:
fdark = Data1d()
wflat = Data1d()
fdark.load_dark_from_2D(["Feb09-dark-00.300s_WAXS",
"Feb09-dark-01.300s_WAXS",
... | code_fim | hard | {
"lang": "python",
"repo": "NSLS-II-LIX/pyXS",
"path": "/examples/Example.Sol/exp_setup.py",
"mode": "spm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NSLS-II-LIX/pyXS path: /examples/Example.Sol/exp_setup.py
import os
try:
import cPickle as pk
except ImportError:
import pickle as pk
from pyxs.DetectorConfig import DetectorConfig
from pyxs.ext.RQconv import *
from pyxs.slnXS import *
es = ExpPara()
es.wavelength = 0.874
es.bm_ctr_x =... | code_fim | hard | {
"lang": "python",
"repo": "NSLS-II-LIX/pyXS",
"path": "/examples/Example.Sol/exp_setup.py",
"mode": "psm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: darkmatter2222/pathfidner-ML path: /bots/race_to_the_hot_fog_of_war/execute_trained_model.py
import tensorflow as tf
from tf_agents.agents.dqn import dqn_agent
from tf_agents.environments import tf_py_environment
from tf_agents.networks import q_network
from tf_agents.policies import random_tf_po... | code_fim | hard | {
"lang": "python",
"repo": "darkmatter2222/pathfidner-ML",
"path": "/bots/race_to_the_hot_fog_of_war/execute_trained_model.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> while not time_step.is_last():
action_step = policy.action(time_step)
time_step = environment.step(action_step.action)
episode_return += time_step.reward
total_return += episode_return
history = environment._env.envs[0].score_history
fina... | code_fim | hard | {
"lang": "python",
"repo": "darkmatter2222/pathfidner-ML",
"path": "/bots/race_to_the_hot_fog_of_war/execute_trained_model.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
_eval_py_env = race_to_the_hot(window_name='Testing')
_eval_env = tf_py_environment.TFPyEnvironment(_eval_py_env)
saved_policy = tf.compat.v2.saved_model.load(_save_policy_dir)
avg_return, score = compute_avg_return(_eval_env, saved_policy)
print('Average Return = {0:.2f}, score {1}'.format(avg_return,... | code_fim | hard | {
"lang": "python",
"repo": "darkmatter2222/pathfidner-ML",
"path": "/bots/race_to_the_hot_fog_of_war/execute_trained_model.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yinruiqing/corpus2graph path: /corpus2graph/applications/networkx_wrapper_test.py
import unittest
from corpus2graph.applications import wordpair_generator, networkx_wrapper, graph_generator
from corpus2graph import FileParser, WordPreprocessor, Tokenizer, WordProcessing, \
SentenceProcessing,... | code_fim | hard | {
"lang": "python",
"repo": "yinruiqing/corpus2graph",
"path": "/corpus2graph/applications/networkx_wrapper_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> igt = networkx_wrapper.IGraphWrapper('Test')
gg = graph_generator.GraphGenerator(window_size=3, file_parser=self.data_type,
xml_node_path=None, word_tokenizer='', wtokenizer=Tokenizer.mytok,
remove_numb... | code_fim | hard | {
"lang": "python",
"repo": "yinruiqing/corpus2graph",
"path": "/corpus2graph/applications/networkx_wrapper_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>except KeyboardInterrupt:
scrollphathd.fill(0)
scrollphathd.show()<|fim_prefix|># repo: pimoroni/scroll-phat-hd path: /examples/tests/scroll-test.py
#!/usr/bin/env python
import time
<|fim_middle|>import scrollphathd
scrollphathd.pixel(0, 0, 0.5)
try:
while True:
scrollphathd.scro... | code_fim | medium | {
"lang": "python",
"repo": "pimoroni/scroll-phat-hd",
"path": "/examples/tests/scroll-test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pimoroni/scroll-phat-hd path: /examples/tests/scroll-test.py
#!/usr/bin/env python
import time
<|fim_suffix|>scrollphathd.pixel(0, 0, 0.5)
try:
while True:
scrollphathd.scroll(1, 1)
scrollphathd.show()
time.sleep(0.1)
except KeyboardInterrupt:
scrollphathd.fill... | code_fim | easy | {
"lang": "python",
"repo": "pimoroni/scroll-phat-hd",
"path": "/examples/tests/scroll-test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif args.operator == "f_mult":
print "...using fortran mult..."
res = f_mult(args.a, args.b)
elif args.operator == "c_div":
print "...using c++ div..."
res = c_div(args.a, args.b)
else:
raise ValueError("that's wrong")
print "result"
print re... | code_fim | hard | {
"lang": "python",
"repo": "csyhuang/python-fortran-cpp-template",
"path": "/scripts/myscript.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: csyhuang/python-fortran-cpp-template path: /scripts/myscript.py
#!/usr/bin/env python2.7
"""Template script
"""
import os, sys
from mypackage import py_add, f_mult, c_div, __version__
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(__doc__)
parser.add_arg... | code_fim | medium | {
"lang": "python",
"repo": "csyhuang/python-fortran-cpp-template",
"path": "/scripts/myscript.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kongyew/gpdb path: /src/test/tinc/tincrepo/mpp/gpdb/tests/storage/fts/fts_transitions/test_fts_transitions_03.py
"""
Copyright (c) 2004-Present Pivotal Software, Inc.
This program and the accompanying materials are made available under
the terms of the under the Apache License, Version 2.0 (the ... | code_fim | hard | {
"lang": "python",
"repo": "kongyew/gpdb",
"path": "/src/test/tinc/tincrepo/mpp/gpdb/tests/storage/fts/fts_transitions/test_fts_transitions_03.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> tinctest.logger.info("\n ===============================================")
tinctest.logger.info("\n Starting New Test: test_mirror_resync_postmaster_reset_with_faults ")
tinctest.logger.info("\n ===============================================")
self.mirror_resync_postmaster... | code_fim | hard | {
"lang": "python",
"repo": "kongyew/gpdb",
"path": "/src/test/tinc/tincrepo/mpp/gpdb/tests/storage/fts/fts_transitions/test_fts_transitions_03.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> * The first batch of changed blocks obtained by resync worker
from CT log for this relation contains only lower
(according to block number) blocks. The higher block with
lower LSN is not included in this batch. Another query
must be run against CT log... | code_fim | hard | {
"lang": "python",
"repo": "kongyew/gpdb",
"path": "/src/test/tinc/tincrepo/mpp/gpdb/tests/storage/fts/fts_transitions/test_fts_transitions_03.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if len(np.asarray(reward).shape) == 1:
length = len(reward)
reward = [reward]
else:
length = len(reward[0])
self.assertAllClose(
np.squeeze(discount_py(reward, gamma=gamma)), expected)
self.assertAllClose(
t(array_ops.squeeze(core_ops.dis... | code_fim | hard | {
"lang": "python",
"repo": "wenkesj/alchemy",
"path": "/alchemy/contrib/rl/core_ops_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_discount(self):
t = self.evaluate
def test_discount_(reward, gamma, expected):
if len(np.asarray(reward).shape) == 1:
length = len(reward)
reward = [reward]
else:
length = len(reward[0])
self.assertAllClose(
np.squeeze(discount_py(re... | code_fim | hard | {
"lang": "python",
"repo": "wenkesj/alchemy",
"path": "/alchemy/contrib/rl/core_ops_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wenkesj/alchemy path: /alchemy/contrib/rl/core_ops_test.py
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.platform import test
from tensorflow.python.ops import array_ops
from alchemy.contr... | code_fim | hard | {
"lang": "python",
"repo": "wenkesj/alchemy",
"path": "/alchemy/contrib/rl/core_ops_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 18F/tock path: /tock/projects/migrations/0028_auto_20210831_1620.py
# Generated by Django 2.2.24 on 2021-08-31 20:20
from django.db import migrations, models
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='project',
name='is_weekly_bill',
... | code_fim | medium | {
"lang": "python",
"repo": "18F/tock",
"path": "/tock/projects/migrations/0028_auto_20210831_1620.py",
"mode": "psm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('projects', '0027_project_is_weekly_bill'),
]
operations = [
migrations.AlterField(
model_name='project',
name='is_weekly_bill',
field=models.BooleanField(default=False, help_text='Is this project under weekly billing. Note... | code_fim | medium | {
"lang": "python",
"repo": "18F/tock",
"path": "/tock/projects/migrations/0028_auto_20210831_1620.py",
"mode": "spm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: anujchavan112/Pipeline_in_machine_learning path: /pipeline_in_machine_learning.py
#!/usr/bin/env python
# coding: utf-8
# In[26]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.read_csv(r'C:\Users\Anuj\Downloads\Project happiness\Project happiness\2019.csv'... | code_fim | hard | {
"lang": "python",
"repo": "anujchavan112/Pipeline_in_machine_learning",
"path": "/pipeline_in_machine_learning.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
pipe_dict={0:"Random Forest regression",1:"Linear Regression",2:"Decesion Tree Regressor "}
# In[146]:
for pipe in pipelines:
pipe.fit(X_train,y_train)
# In[147]:
for i,model in enumerate(pipelines):
print("{} Test acuuracy:{}".format(pipe_dict[i],model.score(X_test,y_test)))
# In[148]:... | code_fim | hard | {
"lang": "python",
"repo": "anujchavan112/Pipeline_in_machine_learning",
"path": "/pipeline_in_machine_learning.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>for pipe in pipelines:
pipe.fit(X_train,y_train)
# In[147]:
for i,model in enumerate(pipelines):
print("{} Test acuuracy:{}".format(pipe_dict[i],model.score(X_test,y_test)))
# In[148]:
model_pipeline_rfgr.predict(X_test)
# In[149]:
model_pipeline_drgr.predict(X_test)
# In[150]:
mod... | code_fim | hard | {
"lang": "python",
"repo": "anujchavan112/Pipeline_in_machine_learning",
"path": "/pipeline_in_machine_learning.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sohonetlabs/dvbcss-synctiming path: /src/dispersion.py
#!/usr/bin/env python
#
# Copyright 2015 British Broadcasting Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the L... | code_fim | hard | {
"lang": "python",
"repo": "sohonetlabs/dvbcss-synctiming",
"path": "/src/dispersion.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def clear(self):
"""\
Clear the recorded history.
"""
self.changeHistory = []
def start(self):
"""\
Start recording changes in dispersion.
If already recording, then this method call does nothing.
"""
... | code_fim | hard | {
"lang": "python",
"repo": "sohonetlabs/dvbcss-synctiming",
"path": "/src/dispersion.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agrawalprash/enaml path: /enaml/backends/qt/noncomponents/qt_icon.py
#------------------------------------------------------------------------------
# Copyright (c) 2012, Enthought, Inc.
# All rights reserved.
#------------------------------------------------------------------------------
from ... | code_fim | hard | {
"lang": "python",
"repo": "agrawalprash/enaml",
"path": "/enaml/backends/qt/noncomponents/qt_icon.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> The returned size may be smaller but will never be larger.
Parameters
----------
size : (width, height)
The size of the requested image. The returned image may
be smaller, but will never be larger than this size.
mode : string, ... | code_fim | hard | {
"lang": "python",
"repo": "agrawalprash/enaml",
"path": "/enaml/backends/qt/noncomponents/qt_icon.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> state : string, optional
The state of the image. The default is 'on'.
"""
if not isinstance(image, QtImage):
msg = 'Image must be an instance of QtImage. Got %s instead.'
raise TypeError(msg % type(image))
qpixmap = image.as_QPix... | code_fim | hard | {
"lang": "python",
"repo": "agrawalprash/enaml",
"path": "/enaml/backends/qt/noncomponents/qt_icon.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Evaluate log probabilities for given inputs.
:param xy: (x, y) pair of numpy arrays, rows are datapoints
:param log: whether to return probabilities in the log domain
:return: list of log probabilities log p(y|x)
"""
# compile theano function, i... | code_fim | hard | {
"lang": "python",
"repo": "gpapamak/maf",
"path": "/ml/models/nvps.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gpapamak/maf path: /ml/models/nvps.py
import numpy as np
import numpy.random as rng
import theano
import theano.tensor as tt
import ml.models.neural_nets as nn
from ml.models.layers import BatchNorm
import util
dtype = theano.config.floatX
class CouplingLayer:
"""
Coupling layer for R... | code_fim | hard | {
"lang": "python",
"repo": "gpapamak/maf",
"path": "/ml/models/nvps.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> lprob = self.eval_lprob_f(x.astype(dtype))
return lprob if log else np.exp(lprob)
def gen(self, n_samples=1, u=None):
"""
Generate samples.
:param n_samples: number of samples
:param u: random numbers to use in generating samples; if None, new random n... | code_fim | hard | {
"lang": "python",
"repo": "gpapamak/maf",
"path": "/ml/models/nvps.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
with pytest.raises(ValueError):
MockPlugin()<|fim_prefix|># repo: magnologan/detect-secrets path: /tests/plugins/base_test.py
from __future__ import absolute_import
import pytest
from detect_secrets.plugins.base import BasePlugin
def test_fails_if_no_secret_type_defined(... | code_fim | medium | {
"lang": "python",
"repo": "magnologan/detect-secrets",
"path": "/tests/plugins/base_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: magnologan/detect-secrets path: /tests/plugins/base_test.py
from __future__ import absolute_import
import pytest
from detect_secrets.plugins.base import BasePlugin
<|fim_suffix|> class MockPlugin(BasePlugin): # pragma: no cover
def analyze_string_content(self, *args, **kwargs):
... | code_fim | easy | {
"lang": "python",
"repo": "magnologan/detect-secrets",
"path": "/tests/plugins/base_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> while True:
sock, addr = s.accept()
t = threading.Thread(target = server, args = (sock, addr))
t.start()<|fim_prefix|># repo: Sherlock-Holo/Python-3.5-learning path: /tcp/server.py
import socket
import threading
def server(sock, addr):
print('connection from %s:%s' %addr)... | code_fim | medium | {
"lang": "python",
"repo": "Sherlock-Holo/Python-3.5-learning",
"path": "/tcp/server.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(('127.0.0.1', 8080))
s.listen(5)
while True:
sock, addr = s.accept()
t = threading.Thread(target = server, args = (sock, addr))
t.start()<|fim_prefix|># repo: Sherlock-Holo/Python-3.5-learning path: /... | code_fim | medium | {
"lang": "python",
"repo": "Sherlock-Holo/Python-3.5-learning",
"path": "/tcp/server.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Sherlock-Holo/Python-3.5-learning path: /tcp/server.py
import socket
import threading
def server(sock, addr):
print('connection from %s:%s' %addr)
buffer = []
while True:
d = sock.recv(4096)
if not d:
break
buffer.append(d)
data = b''.join(buf... | code_fim | medium | {
"lang": "python",
"repo": "Sherlock-Holo/Python-3.5-learning",
"path": "/tcp/server.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> paths = extracting.get_paths([self.test_files])
data = extracting.extract_data(paths)
for node in data:
self.assertEqual(node, [])<|fim_prefix|># repo: xenking/lightdataparser path: /lightdataparser/tests/test_extract.py
import unittest
from unittest.mock import patch
... | code_fim | hard | {
"lang": "python",
"repo": "xenking/lightdataparser",
"path": "/lightdataparser/tests/test_extract.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xenking/lightdataparser path: /lightdataparser/tests/test_extract.py
import unittest
from unittest.mock import patch
from lightdataparser import extracting
class TestExtractProcess(unittest.TestCase):
def setUp(self):
with open("filepaths.txt", 'r') as f:
self.bad_file... | code_fim | hard | {
"lang": "python",
"repo": "xenking/lightdataparser",
"path": "/lightdataparser/tests/test_extract.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> empty_path = extracting.get_out_path('')
paths = [extracting.get_out_path(file) for file in self.bad_files]
folder_path = extracting.get_out_path(self.test_files)
@patch('lightdataparser.extracting.parse', return_value=({'A': 1, 'B': 0, 'C': 3, 'D': 2}, [3, 2, 1, 4]))
@pat... | code_fim | hard | {
"lang": "python",
"repo": "xenking/lightdataparser",
"path": "/lightdataparser/tests/test_extract.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> import *<|fim_prefix|># repo: muteria/muteria path: /muteria/drivers/testgeneration/testcase_formats/python_unittest/__init__.py
from muteria.drivers.testgeneration.te<|fim_middle|>stcase_formats.python_unittest.unittest\
| code_fim | hard | {
"lang": "python",
"repo": "muteria/muteria",
"path": "/muteria/drivers/testgeneration/testcase_formats/python_unittest/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: muteria/muteria path: /muteria/drivers/testgeneration/testcase_formats/python_unittest/__init__.py
from muteria.drivers.testgeneration.te<|fim_suffix|>\
import *<|fim_middle|>stcase_formats.python_unittest.unittest | code_fim | easy | {
"lang": "python",
"repo": "muteria/muteria",
"path": "/muteria/drivers/testgeneration/testcase_formats/python_unittest/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: intel/ThenWhatTree path: /ThenWhatTree/lib/twt_node/node_metrics_data.py
# Copyright (C) 2018 Intel Corporation
# SPDX-License-Identifier: BSD-3-Clause
<|fim_suffix|>
# Code starts here
class NodeMetricsData(object):
pass<|fim_middle|>"""Module description here"""
# Import built in modul... | code_fim | hard | {
"lang": "python",
"repo": "intel/ThenWhatTree",
"path": "/ThenWhatTree/lib/twt_node/node_metrics_data.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|># Import local modules
# Module authorship metadata
__author__ = "Erik W Berg"
__copyright__ = "Copyright 2018, Intel Corporation"
__credits__ = [""]
__license__ = "BSD-3-Clause"
__version__ = "1.0"
__maintainer__ = "Erik W Berg"
__email__ = ""
__status__ = "Production" # Prototype, Development, Product... | code_fim | medium | {
"lang": "python",
"repo": "intel/ThenWhatTree",
"path": "/ThenWhatTree/lib/twt_node/node_metrics_data.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tern-tools/tern path: /tern/formats/spdx/spdxjson/consumer.py
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 VMware, Inc. All Rights Reserved.
# SPDX-License-Identifier: BSD-2-Clause
"""
SPDXJSON document consumer
"""
import json
import logging
import os
from tern.classes.image_layer import Im... | code_fim | hard | {
"lang": "python",
"repo": "tern-tools/tern",
"path": "/tern/formats/spdx/spdxjson/consumer.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Given a list of report files in the SPDX JSON format, created by
the spdxjson generator, create a total list of image layer objects.
We assume the layers are ordered in the order or report files"""
layer_list = []
layer_count = 1
for report in reports:
... | code_fim | hard | {
"lang": "python",
"repo": "tern-tools/tern",
"path": "/tern/formats/spdx/spdxjson/consumer.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kids-first/kf-api-study-creator path: /creator/studies/migrations/0016_add_phenotype_status.py
# Generated by Django 2.1.11 on 2020-05-06 21:18
from django.db import migrations, models
class Migration(migrations.Migration):
<|fim_suffix|> operations = [
migrations.AlterModelOptions... | code_fim | medium | {
"lang": "python",
"repo": "kids-first/kf-api-study-creator",
"path": "/creator/studies/migrations/0016_add_phenotype_status.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterModelOptions(
name='study',
options={'permissions': [('view_my_study', 'Can list studies that the user belongs to'), ('add_collaborator', 'Can add a collaborator to the study'), ('remove_collaborator', 'Can remove a collaborator to the stu... | code_fim | medium | {
"lang": "python",
"repo": "kids-first/kf-api-study-creator",
"path": "/creator/studies/migrations/0016_add_phenotype_status.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> aws_accounts.append(
{
"AccountId": line_items[0],
"AccountName": line_items[1].replace("\n", ""),
}
)
return aws_accounts
def get_accounts(account_id_only=True):
if watchmen_vars.UseAWSOrganisations:
master_linked_a... | code_fim | hard | {
"lang": "python",
"repo": "adrianmkng/watchmen",
"path": "/python_lib/get_accounts.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: adrianmkng/watchmen path: /python_lib/get_accounts.py
# Copyright 2017 Insurance Australia Group Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://w... | code_fim | hard | {
"lang": "python",
"repo": "adrianmkng/watchmen",
"path": "/python_lib/get_accounts.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: skwang/trafficbehavior path: /common/display.py
import cv2, sys
from trafficbehavior.common import image_util
def draw_lanes(img, lanes, color=(0,255,0), thickness=3):
for lane in lanes:
[pt1, pt2] = lane
pt1 = (int(pt1[0]), int(pt1[1]))
pt2 = (int(pt2[0]), int(pt2[1]... | code_fim | hard | {
"lang": "python",
"repo": "skwang/trafficbehavior",
"path": "/common/display.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> toolbar_width = 40
fname = filenames[curr_index]
progress = float(curr_index)/len(filenames)
percent_str = "{}%".format(int(progress*100))
# setup toolbar
sys.stdout.write("\r{}|{}|{}".format(fname, " "*toolbar_width, percent_str))
sys.stdout.flush()
# goes back to right be... | code_fim | hard | {
"lang": "python",
"repo": "skwang/trafficbehavior",
"path": "/common/display.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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